mlsanityData to 7 Oct 2026

Causal inference in drug discovery and development

arXiv 2209.14664 ↗First posted 29 Sep 2022q-bio.QM cs.LG stat.APcited by 6

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Abstract (arXiv)

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision making in drug discovery. While it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a non-technical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 165 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

12 of 165 references.

References · 12

  1. 2021Towards Causal Representation Learningcited by 207
  2. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  3. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  4. 2015External Validity: From Do-Calculus to Transportability Across Populationscited by 75
  5. 2021Granger Causality: A Review and Recent Advancescited by 62
  6. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  7. 2020Causal models for dynamical systemscited by 34
  8. 2017Simple rules for complex decisionscited by 14
  9. 2014Multiple Quantitative Trait Analysis Using Bayesian Networkscited by 9
  10. 2021Causal Inference in medicine and in health policy, a summarycited by 4
  11. 2018Estimating the treatment effect in a subgroup defined by an early post-baseline biomarker measurement in randomized clinical trials with time-to-event endpointcited by 1
  12. 2021Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trialscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
  2. 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulantscited by 2
  3. 2025The Minimal Search Space for Conditional Causal Banditscited by 1
  4. 2024ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulationscited by 0
  5. 2025Programmable Virtual Humans Toward Human Physiologically-Based Drug Discoverycited by 0
  6. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0